On Localization Issues of Mobile Devices

Mobile devices, such as sensor nodes, smartphones and smartwatches, are now widely used in many applications. Localization is a highly important topic in wireless networks as well as in many Internet of Things applications. In this thesis, four novel localization schemes of mobile devices are introduced to improve the localization performance in three different areas, like the outdoor, indoor and underwater environments. Firstly, in the outdoor environment, many current localization algorithms are based on the Sequential Monte MCL, the accuracy of which is bounded by the radio range. High computational complexity in the sampling step is another issue of these approaches. Tri-MCL is presented, which significantly improves on the accuracy of the Monte Carlo Localization algorithm. To do this, three different distance measurement algorithms based on range-free approaches are leveraged. Using these, the distances between unknown nodes and anchor nodes are estimated to perform more fine-grained filtering of the particles as well as for weighting the particles in the final estimation step of the algorithm. Simulation results illustrate that the proposed algorithm achieves better accuracy than the MCL and SA-MCL algorithms. Furthermore, it also exhibits high efficiency in the sampling step. Then, in the GPS-denied indoor environment, Twi-Adaboost is proposed, which is a collaborative indoor localization algorithm with the fusion of internal sensors such as the accelerometer, gyroscope and magnetometer from multiple devices. Specifically, the datasets are collected firstly by one person wearing two devices simultaneously: a smartphone and a smartwatch, each collecting multivariate data represented by their internal parameters in a real environment. Then, the datasets from these two devices are evaluated for their strengths and weaknesses in recognizing the indoor position. Based on that, the Twi-AdaBoost algorithm, an interactive ensemble learning method, is proposed to improve the indoor localization accuracy by fusing the co-occurrence information. The performance of the proposed algorithm is assessed on a real-world dataset. The experiment results demonstrate that Twi-AdaBoost achieves a localization error about 0.39 m on average with a low deployment cost, which outperforms the state-of-the-art indoor localization algorithms. Lastly, the characteristics of mobile UWSNs, such as low communication bandwidth, large propagation delay, and sparse deployment, pose challenging issues for successful localization of sensor nodes. In addition, sensor nodes in UWSNs are usually powered by batteries whose replacements introduces high cost and complexity. Thus, the critical problem in UWSNs is to enable each sensor node to find enough anchor nodes in order to localize itself, with minimum energy costs. An Energy-Efficient Localization Algorithm (EELA) is proposed to analyze the decentralized interactions among sensor nodes and anchor nodes. A Single-Leader-Multi-Follower Stackelberg game is utilized to formulate the topology control problem of sensor nodes and anchor nodes by exploiting their available communication opportunities. In this game, the sensor node acts as a leader taking into account factors such as `two-hop' anchor nodes and energy consumption, while anchor nodes act as multiple followers, considering their ability to localize sensor nodes and their energy consumption. I prove that both players select best responses and reach a socially optimal Stackelberg Nash Equilibrium. Simulation results demonstrate that the proposed EELA improves the performance of localization in UWSNs significantly, and in particular the energy cost of sensor nodes. Compared to the baseline schemes, the energy consumption per node is about 48% lower in EELA, while providing a desirable localization coverage, under reasonable error and delay. Based on the EELA scheme, an Adaptive Energy Efficient Localization Algorithm using the Fuzzy game theoretic method (Adaptive EELA) is proposed to solve the environment adaptation problem of EELA. The adaptive neuro-fuzzy method is used as the utility function of the Single-Leader-Multi-Follower Stackelberg game to model the dynamical changes in UWSNs. The proposed Adaptive EELA scheme is able to automatically learn in the offline phase, which is required only once. Then, in the online phase, it can adapt to the environmental changes, such as the densities of nodes or topologies of nodes. Extensive numerical evaluations are conducted under different network topologies and different network node densities. The simulation results demonstrate that the proposed Adaptive EELA scheme achieves about 35% and 66% energy reduction per node on average comparing the state-of-the-art approaches, such as EELA and OLTC, while providing a desirable localization coverage, localization error and localization delay